Models for Bundle Trading in Financial Markets

Bundle trading is a new trend in financial markets that allows traders to submit consolidated orders to sell and buy packages of assets. We propose a new formulation for portfolio bundle trading that extends the previous models of the literature through a more detailed representation of portfolios and the formulation of new bidding requirements. We also present post-optimality tie-breaking procedures intended to discriminate equivalent orders on the basis of their submission times. Numerical results evaluate the "bundle" effect as well as the bidding flexibility and the computational complexity of our formulation. Une nouvelle tendance dans les marchés financiers consiste à transiger des valeurs financières sous forme d'ordres composites d'achat et de vente. Nous proposons une nouvelle formulation basée sur les ordres composites du problème d'allocation de valeurs financières. Notre modèle, comparativement à ceux de la littérature, permet une représentation plus détaillée des portefeuilles financiers et la formulation de nouvelles contraintes transactionnelles. Nous présentons en outre une procédure de discrimination d'ordres équivalents sur la base de leur temps de soumission. Les résultats numériques de notre étude permettent d'évaluer empiriquement l'effet « ordres composites », ainsi que la flexibilité et la complexité numérique de notre formulation.Auction Design, Financial Markets, Bundle Trading, Discrimination Procedures, Mécanisme d'enchères, marchés financiers, ordres composites, procédures de discrimination

Design for Optimized Multi-Lateral Multi-Commodity Markets

In this paper, we propose a design for an an economically efficient, optimized, centralized, multi-lateral, periodic commodity market that addresses explicitly three issues: (i) substantial transportation costs between sellers and buyers; (ii) non homogeneous, in quality and nature, commodities; (iii) complementary commodities that have to be traded simultaneously. The model allows sellers to offer their commodities in lots and buyers to explicitly quantify the differences in quality of the goods produced by each individual seller. The model does not presume that products must be shipped through a market hub. We also propose a multi-round auction that enables the implementation of the direct optimized market and approximates the behaviour of the "ideal" direct optimized mechanism. The process allows buyers and sellers to modify their initial bids, including the technological constraints. The proposed market designs are particularly relevant for industries related to natural resources. We present the models and algorithms required to implement the optimized market mechanisms, describe the operations of the multi-round auction, and discuss applications and perspectives. Nous présentons un concept de marché optimisé, centralisé, multilatéral et périodique pour l'acquisition de produits qui traite explicitement les trois aspects suivants: (i) des coûts de transport importants des vendeurs vers les acheteurs; (ii) des produits non homogènes en valeur et qualité; des complémentarités entre les divers produits qui doivent donc être négociés simultanément. Le modèle permet aux vendeurs d'offrir leurs produits groupés en lots et aux acheteurs de quantifier explicitement leur évaluation des lots mis sur le marché par chaque vendeur. Le modèle ne suppose pas que les produits doivent être expédiés par un centre avant d'être livrés. Nous proposons également un mécanisme de tâtonnement à rondes multiples qui approxime le comportement du marché direct optimisé et qui permet de mettre ce dernier en oeuvre. Le processus de tâtonnement permet aux vendeurs et aux acheteurs de modifier leurs mises initiales, incluant les contraintes technologiques. Les concepts proposés sont particulièrement adaptés aux industries reliées aux matières premières. Nous présentons les modèles et algorithmes requis à la mise en oeuvre du marché multi-latéral optimisé, nous décrivons le fonctionnement du processus de tâtonnement, et nous discutons les applications et perspectives reliées à ces mécanismes de marché.Market design, optimized multi-lateral multi-commodity markets, multi-round auctions, Design de marché, marché multi-latéraux optimisés, processus de tâtonnement

Assortative-Constrained Stochastic Block Models

Stochastic block models (SBMs) are often used to find assortative community structures in networks, such that the probability of connections within communities is higher than in between communities. However, classic SBMs are not limited to assortative structures. In this study, we discuss the implications of this model-inherent indifference towards assortativity or disassortativity, and show that this characteristic can lead to undesirable outcomes for networks which are presupposedy assortative but which contain a reduced amount of information. To circumvent this issue, we introduce a constrained SBM that imposes strong assortativity constraints, along with efficient algorithmic approaches to solve it. These constraints significantly boost community recovery capabilities in regimes that are close to the information-theoretic threshold. They also permit to identify structurally-different communities in networks representing cerebral-cortex activity regions

An adaptive large neighborhood search for a vehicle routing problem with cross-dock under dock resource constraints

International audienceIn this work, we study the impact of dock resource constraints on the cost of VRPCD solutions

A unifying framework for fairness-aware influence maximization

The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studied over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms